稀疏表达的运动数据压缩

随着运动数据越来越多地被应用于动画制作和科研领域,高效的运动数据压缩技术也逐渐成为一个热门的研究课题.基于稀疏表达提出一种新的运动数据有损压缩方法.首先对输入的运动数据进行分析生成稀疏表达字典;然后基于稀疏表达字典对运动数据中的每一帧进行稀疏线性表达;最后用K-SVD算法对字典和稀疏表示进行迭代优化.实验结果表明,本文方法可以达到较高的压缩比(50倍左右),同时保持原始运动数据的完整性,还原后可控制重建误差在肉眼不易分辨的范围内(平均RMS误差2.0以下),并且本文方法特别适用于对较短运动数据的压缩.     

LZSS文本压缩自满实现与研究

设计实现了LZSS压缩算法，为了适合于中文压缩，作者对其进行了改进，通过测试证明改进是有效的，相比于标准LZSS压缩算法，压缩比有了很大幅度的提高，对于中文文本长文件，其最大压缩比已达到20左右，对于英文文本文件的压缩效果也好于LZSS12算法；同时，得出了LXSS算法的极限压缩率，有重要的实际应用价值。     

CCIGS: A data compression system for Chinese fonts and binary images using classification techniques

To achieve a high compression ratio when storing a large amount of Chinese characters is a problem in applications using Chinese characters. In this paper, we present a data compression software system which can reduce the storage requirement of Chinese characters and binary images. This system is composed of a Chinese font compressing subsystem and a binary image compressing subsystem. In the Chinese font compressing subsystem we have improved the traditional methods of feature extraction and classification techniques so as to achieve a high compression ratio in the compression of Chinese characters. Based on the technique of this subsystem, we have combined the block segmentation technique to compress a binary image. In addition to achieving a good data compression ratio the encoding-decoding process is computationally very efficient in our system. We also show that with our system the quality and accuracy of the reconstructed pattern and image are very close to those of the original pattern and image.     

A data mining approach to database compression

Data mining can dig out valuable information from databases to assist a business in approaching knowledge discovery and improving business intelligence. Database stores large structured data. The amount of data increases due to the advanced database technology and extensive use of information systems. Despite the price drop of storage devices, it is still important to develop efficient techniques for database compression. This paper develops a database compression method by eliminating redundant data, which often exist in transaction database. The proposed approach uses a data mining structure to extract association rules from a database. Redundant data will then be replaced by means of compression rules. A heuristic method is designed to resolve the conflicts of the compression rules. To prove its efficiency and effectiveness, the proposed approach is compared with two other database compression methods. Chin-Feng Lee is an associate professor with the Department of Information Management at Chaoyang University of Technology, Taiwan, R.O.C. She received her M.S. and Ph.D. degrees in 1994 and 1998, respectively, from the Department of Computer Science and Information Engineering at National Chung Cheng University. Her current research interests include database design, image processing and data mining techniques. S. Wesley Changchien is a professor with the Institute of Electronic Commerce at National Chung-Hsing University, Taiwan, R.O.C. He received a BS degree in Mechanical Engineering (1989) and completed his MS (1993) and Ph.D. (1996) degrees in Industrial Engineering at State University of New York at Buffalo, USA. His current research interests include electronic commerce, internet/database marketing, knowledge management, data mining, and decision support systems. Jau-Ji Shen received his Ph.D. degree in Information Engineering and Computer Science from National Taiwan University at Taipei, Taiwan in 1988. From 1988 to 1994, he was the leader of the software group in Institute of Aeronautic, Chung-Sung Institute of Science and Technology. He is currently an associate professor of information management department in the National Chung Hsing University at Taichung. His research areas focus on the digital multimedia, database and information security. His current research areas focus on data engineering, database techniques and information security. Wei-Tse Wang received the B.A. (2001) and M.B.A (2003) degrees in Information Management at Chaoyang University of Technology, Taiwan, R.O.C. His research interests include data mining, XML, and database compression.     

基于动态规划算法的矢量数据压缩改进算法

为了使移动设备存储大容量的矢量数据和提高矢量数据的网络传输效率,矢量数据压缩是一项很重要的工作。提出了基于动态规划算法的矢量数据压缩的模型和改进方法,通过一条参考路径构造一条带形成最小误差搜索范围,同时条带宽度可自适应调整。实验结果表明,该方法具有较高的效率,能够得到较小的压缩误差。     

计算机互联网络中的数据压缩技术应用

通过设计一个嵌入数据压缩技术的网络通信程序，实现了通过网络的对于用户透明的压缩数据传输，应用程序中实现了Huffman,LZSS，LZW3种压缩算法，将压缩成功地应用于文件传输过程，发送方发送文件时自动将文件压缩并传输到网络上，接收端则在接收文件的同时自动解压缩，从而减少了需要在网络中传输的数据量。     

On data mining,compression, and Kolmogorov complexity

Will we ever have a theory of data mining analogous to the relational algebra in databases? Why do we have so many clearly different clustering algorithms? Could data mining be automated? We show that the answer to all these questions is negative, because data mining is closely related to compression and Kolmogorov complexity; and the latter is undecidable. Therefore, data mining will always be an art, where our goal will be to find better models (patterns) that fit our datasets as best as possible.     

NC格式气象数据无损压缩研究

针对相邻时次气象数据的相关性,提出了在时间尺度上对相邻时次格点数据进行减运算,使数据高位尽可能出现00值或FF值,在空间尺度上对同时次相邻格点数据再进行减运算,使数据高位也尽可能出现00值或FF值,应用WinRAR无损压缩工具,实现无损压缩的算法。实验结果证实,这种压缩方法,可以有效地提高压缩比,实现气象格点数据的完全无损压缩。     

基于HBase的列存储压缩策略的选择优化

大数据时代背景下,列存储数据库使用场景愈加增多,推动了列存储相关领域的研究进展。为解决现有列存储数据库压缩策略在压缩过程中遇到的数据离散程度大,分类粒度小,配套分类算法缺陷导致的学习成本高,压缩效率难以保证的问题,本文提出了一种基于排序的列区混合压缩策略,首先根据HBase特点设计了一种对各列数据进行排序的方法加强数据紧密度,然后根据数据特点分别使用混级区压缩策略和混级列压缩策略进行压缩策略推荐,在TPC-DS标准数据集上与前人策略进行比较,实验结果显示本文方法在压缩率、压缩/解压时间方面均有优异的表现,从而证明了本文方法的有效性。     

无线传感器网络簇首提取压缩算法

对矢量数据压缩算法中DP压缩算法在引入无线传感器网络的同时进行了改进,针对压缩过程中对数据的扫描次数问题,提出簇首提取压缩算法。该算法中“簇首”即为“数据簇首”,簇首提取压缩算法设定步长减少压缩过程中对数据的扫描次数,并采用最佳曲线拟合方法对监测数据点做直线优化拟合,根据数据间的依附关系,将体现整体特征的簇首数据进行提取;同时,对非簇首数据进行子群划分。仿真结果表明,簇首提取压缩算法程序更为简单,对大波动数据有较好的簇首提取效果,减少了网络中数据的传输量,有效地节省了整个网络的能量消耗。     

Retrieval of crop chlorophyll content and leaf area index from decompressed hyperspectral data: the effects of data compression

The objective of this study is to evaluate whether the retrieval of the leaf chlorophyll content and leaf area index (LAI) for precision agriculture application from hyperspectral data is significantly affected by data compression. This analysis was carried out using the hyperspectral data sets acquired by Compact Airborne Spectrographic Imager (CASI) over corn fields at L'Acadie experimental farm (Agriculture and Agri-Food Canada) during the summer of 2000 and over corn, soybean and wheat fields at the former Greenbelt farm (Agriculture and Agri-Food Canada) in three intensive field campaigns during the summer of 2001. Leaf chlorophyll content and LAI were retrieved from the original data and the reconstructed data compressed/decompressed by the compression algorithm called Successive approximation multi-stage vector quantization (SAMVQ) at compression ratios of 20:1, 30:1, and 50:1. The retrieved products were evaluated against the ground-truth.In the retrieval of leaf chlorophyll content (the first data set), the spatial patterns were examined in all of the images created from the original and reconstructed data and were proven to be visually unchanged, as expected. The data measures R², absolute RMSE, and relative RMSE between the leaf chlorophyll content derived from the original and reconstructed data cubes, and the laboratory-measured values were calculated as well. The results show the retrieval accuracy of crop chlorophyll content is not significantly affected by SAMVQ at the compression ratios of 20:1, 30:1, and 50:1, relative to the observed uncertainties in ground truth values. In the retrieval of LAI (the second data set), qualitative and quantitative analyses were performed. The results show that the spatial and temporal patterns of the LAI images are not significantly affected by SAMVQ and the retrieval accuracies measured by the R², absolute RMSE, and relative RMSE between the ground-measured LAI and the estimated LAI are not significantly affected by the data compression either.     

A module-based LSB substitution method with lossless secret data compression

All the various data hiding methods can be simply divided into two types: (1) the extracted important data are lossy, (2) the extracted important data are lossless. The proposed method belongs to the second type. In this paper, a module-based substitution method with lossless secret data compression function is used for concealing smoother area of secret image by modifying fewer pixels in the generated stego-image. Compared with the previous data hiding methods that extract lossless data, the generated stego-image by the proposed method is always with better quality, unless the hidden image is with very strong randomness.     

Dynamic Fractal Transform with Applications to Image Data Compression

A recent trend in computer graphics and image processing is to use Iterated Function System(IFS)to generate and describe both man-made graphics and natural images.Jacquin was the first to propose a fully automation gray scale image compression algorithm which is referred to as a typical static fractal transform based algorithm in this paper.By using this algorithm,an image can be condensely described as a fractal transform operator which is the combination of a set of reactal mappings.When the fractal transform operator is iteratedly applied to any initial image,a unique attractro(reconstructed image)can be achieved.In this paper,a dynamic fractal transform is presented which is a modification of the static transform.Instea of being fixed,the dynamic transform operator varies in each decoder iteration,thus differs from static transform operators.The new transform has advantages in improving coding efficiency and shows better convergence for the deocder.     

Lossy image compression using singular value decomposition and wavelet difference reduction

This paper presents a new lossy image compression technique which uses singular value decomposition (SVD) and wavelet difference reduction (WDR). These two techniques are combined in order for the SVD compression to boost the performance of the WDR compression. SVD compression offers very high image quality but low compression ratios; on the other hand, WDR compression offers high compression. In the Proposed technique, an input image is first compressed using SVD and then compressed again using WDR. The WDR technique is further used to obtain the required compression ratio of the overall system. The proposed image compression technique was tested on several test images and the result compared with those of WDR and JPEG2000. The quantitative and visual results are showing the superiority of the proposed compression technique over the aforementioned compression techniques. The PSNR at compression ratio of 80:1 for Goldhill is 33.37 dB for the proposed technique which is 5.68 dB and 5.65 dB higher than JPEG2000 and WDR techniques respectively.     

A novel lossless data compression scheme based on the error correcting Hamming codes

This paper introduces a novel lossless binary data compression scheme that is based on the error correcting Hamming codes, namely the HCDC scheme. In this scheme, the binary sequence to be compressed is divided into blocks of n bits length. To utilize the Hamming codes, the block is considered as a Hamming codeword that consists of p parity bits and d data bits (n=d+p). Then each block is tested to find if it is a valid or a non-valid Hamming codeword. For a valid block, only the d data bits preceded by 1 are written to the compressed file, while for a non-valid block all n bits preceded by 0 are written to the compressed file. These additional 1 and 0 bits are used to distinguish the valid and the non-valid blocks during the decompression process. An analytical formula is derived for computing the compression ratio as a function of block size, and fraction of valid data blocks in the sequence. The performance of the HCDC scheme is analyzed, and the results obtained are presented in tables and graphs. Finally, conclusions and recommendations for future works are pointed out.     

基于改进科学计算浮点数压缩算法的工业远程监控数据无损压缩方法

为解决大量工业远程监控数据在通用分组无线服务(GPRS)网络上的传输延迟问题,提出了基于改进科学计算浮点数压缩(FPC)算法的工业远程监控数据无损压缩方法.首先,根据工业监控数据中浮点数部分的特点对原FPC算法中的预测器结构进行改进,并将该改进算法作为浮点数部分的压缩算法; 然后,与区间编码相结合作为整个数据域的压缩方法.改进前后的浮点数部分压缩实验结果表明改进的FPC算法提高了预测器的预测精度,且在保持较高压缩效率的同时提高了压缩率.与通用无损压缩算法相比,所提算法提高了12%以上的平均压缩率,减少了38.5%以上的平均压缩时间,使得传输时间降低了23.7%以上,在传输数据量大且传输速率不高的情况下大大提高了监控的实时性.     

Practical data compression in wireless sensor networks: A survey

Power consumption is a critical problem affecting the lifetime of wireless sensor networks. A number of techniques have been proposed to solve this issue, such as energy-efficient medium access control or routing protocols. Among those proposed techniques, the data compression scheme is one that can be used to reduce transmitted data over wireless channels. This technique leads to a reduction in the required inter-node communication, which is the main power consumer in wireless sensor networks. In this article, a comprehensive review of existing data compression approaches in wireless sensor networks is provided. First, suitable sets of criteria are defined to classify existing techniques as well as to determine what practical data compression in wireless sensor networks should be. Next, the details of each classified compression category are described. Finally, their performance, open issues, limitations and suitable applications are analyzed and compared based on the criteria of practical data compression in wireless sensor networks.     

一种过程数据有损压缩比的动态控制方法

对于涉及使用压缩偏差[ΔE]的有损压缩算法（如旋转门算法、矩形波串法、后向斜率法）,一般需要长时间的实验才能得到合适的[ΔE]。对于测点较多系统简直是一种灾难。因此,[ΔE]的设置严重限制了压缩算法的实用性和方便性。鉴于此,提出了以压缩比CR（CR=1－压缩后的数据个数/压缩前的数据个数）为导向的动态控制方法。它假定压缩比CR和压缩偏差[ΔE]之间存在线性关系。在给定目标压缩比[CRdest]的条件下,借助于先前一段数据压缩情况,在可调区间[ΔEmin,ΔEmax]上动态地调整[ΔE],使实际[CR→CRdest]。该方法的特点在于使得压缩比可控,同时降低了相关压缩算法参数设置难度。     

Toward an abstract theory of data compression

This paper extends the usual notion of abstract program size complexity, studied by Kolmogorov, Chaitin and others, to a theory that can better model the concept of a ‘practical’ compression method. The contraction of a string is defined, as in standard program size complexity, to be the shortest program which produces that string. However, this is in general an undecidable problem. Here, a model for an abstract compression ‘scheme’ is proposed. An abstract compression scheme not only allows the programming language and cost function to be specified, but also a restricted domain of programs that may be used as compressed forms. Limitations and inherent trade-offs are discussed and a class of ‘good’ schemes is considered.